{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'axes.linewidth': 1.5,\n",
       " 'axes.unicode_minus': False,\n",
       " 'figure.dpi': 100,\n",
       " 'font.size': 12,\n",
       " 'legend.frameon': True,\n",
       " 'legend.handletextpad': 0.4,\n",
       " 'legend.handlelength': 1,\n",
       " 'legend.facecolor': 'white',\n",
       " 'legend.fancybox': True,\n",
       " 'legend.fontsize': 8,\n",
       " 'mathtext.default': 'regular',\n",
       " 'savefig.bbox': 'tight',\n",
       " 'xtick.labelsize': 10,\n",
       " 'ytick.labelsize': 10,\n",
       " 'xtick.major.size': 4,\n",
       " 'ytick.major.size': 4,\n",
       " 'xtick.major.width': 1,\n",
       " 'ytick.major.width': 1,\n",
       " 'xtick.top': True,\n",
       " 'ytick.right': True,\n",
       " 'axes.edgecolor': 'black',\n",
       " 'savefig.facecolor': 'white',\n",
       " 'axes.facecolor': 'whitesmoke',\n",
       " 'font.family': 'sans',\n",
       " 'font.monospace': 'computer modern roman',\n",
       " 'text.usetex': True,\n",
       " 'axes.grid': True,\n",
       " 'grid.color': 'gray',\n",
       " 'grid.linestyle': '-',\n",
       " 'grid.linewidth': 0.2,\n",
       " 'grid.alpha': 0.3,\n",
       " 'axes.axisbelow': 'line'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from visual import setting as se\n",
    "import matplotlib.pyplot as plt\n",
    "se.set_rc_params_jup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Washington's Non-Conflictual Nature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "usleader = pd.read_csv('US-Leader-Mids.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i1</th>\n",
       "      <th>i2</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.595898</td>\n",
       "      <td>0.261838</td>\n",
       "      <td>0.400014</td>\n",
       "      <td>0.190117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.102647</td>\n",
       "      <td>0.056851</td>\n",
       "      <td>0.075772</td>\n",
       "      <td>0.050972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.443038</td>\n",
       "      <td>0.148718</td>\n",
       "      <td>0.229122</td>\n",
       "      <td>0.057637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.515713</td>\n",
       "      <td>0.229583</td>\n",
       "      <td>0.350497</td>\n",
       "      <td>0.163208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.581395</td>\n",
       "      <td>0.246710</td>\n",
       "      <td>0.418182</td>\n",
       "      <td>0.200444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.664609</td>\n",
       "      <td>0.308404</td>\n",
       "      <td>0.449920</td>\n",
       "      <td>0.224453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.801170</td>\n",
       "      <td>0.367521</td>\n",
       "      <td>0.555056</td>\n",
       "      <td>0.308240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              i1         i2         p1         p2\n",
       "count  43.000000  43.000000  43.000000  43.000000\n",
       "mean    0.595898   0.261838   0.400014   0.190117\n",
       "std     0.102647   0.056851   0.075772   0.050972\n",
       "min     0.443038   0.148718   0.229122   0.057637\n",
       "25%     0.515713   0.229583   0.350497   0.163208\n",
       "50%     0.581395   0.246710   0.418182   0.200444\n",
       "75%     0.664609   0.308404   0.449920   0.224453\n",
       "max     0.801170   0.367521   0.555056   0.308240"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usleader[['i1', 'i2', 'p1', 'p2']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i1    0.600000\n",
       "i2    0.242857\n",
       "p1    0.448276\n",
       "p2    0.215971\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usleader.loc[usleader.lastname==\"Washington\"][['i1', 'i2', 'p1', 'p2']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "i1wash, i2wash, p1wash, p2wash = usleader.loc[usleader.lastname==\"Washington\"][['i1', 'i2', 'p1', 'p2']].mean()\n",
    "i1, i2, p1, p2 = usleader[['i1', 'i2', 'p1', 'p2']].mean()\n",
    "std_i1, std_i2, std_p1, std_p2 = usleader[['i1', 'i2', 'p1', 'p2']].std()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "z = (rawdata-popmean)/standard_deviation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_i1 = (i1wash-i1)/std_i1\n",
    "z_i2 = (i2wash-i2)/std_i2\n",
    "z_p1 = (p1wash-p1)/std_p1\n",
    "z_p2 = (p2wash-p2)/std_p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Washington Average I1 = 0.6, with the overall average = 0.5959, Z Score for I1 =  0.04\n",
      "Washington Average I2 = 0.2429, with the overall average = 0.2618, Z Score for I2 =  -0.3339\n",
      "Washington Average p1 = 0.4483, with the overall average = 0.4, Z Score for p1 =  0.6369\n",
      "Washington Average p2 = 0.216, with the overall average = 0.1901, Z Score for p2 =  0.5072\n"
     ]
    }
   ],
   "source": [
    "print(\"Washington Average I1 = \" + str(round(i1wash, 4)) + \", with the overall average = \" + str(round(i1, 4)) + \", Z Score for I1 = \", round(z_i1,4), )\n",
    "print(\"Washington Average I2 = \" + str(round(i2wash, 4)) + \", with the overall average = \" + str(round(i2, 4)) + \", Z Score for I2 = \", round(z_i2,4), )\n",
    "print(\"Washington Average p1 = \" + str(round(p1wash, 4)) + \", with the overall average = \" + str(round(p1, 4)) + \", Z Score for p1 = \", round(z_p1,4), )\n",
    "print(\"Washington Average p2 = \" + str(round(p2wash, 4)) + \", with the overall average = \" + str(round(p2, 4)) + \", Z Score for p2 = \", round(z_p2,4), )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nixon's Conceptual Complexity Changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lastname</th>\n",
       "      <th>firstname</th>\n",
       "      <th>Ccode</th>\n",
       "      <th>Word Count</th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>cc</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>Richard</td>\n",
       "      <td>2</td>\n",
       "      <td>44891</td>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>0.655584</td>\n",
       "      <td>Richard Nixon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>Richard</td>\n",
       "      <td>2</td>\n",
       "      <td>38086</td>\n",
       "      <td>1969</td>\n",
       "      <td>2</td>\n",
       "      <td>0.638992</td>\n",
       "      <td>Richard Nixon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>Richard</td>\n",
       "      <td>2</td>\n",
       "      <td>30950</td>\n",
       "      <td>1969</td>\n",
       "      <td>3</td>\n",
       "      <td>0.594854</td>\n",
       "      <td>Richard Nixon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>Richard</td>\n",
       "      <td>2</td>\n",
       "      <td>27926</td>\n",
       "      <td>1969</td>\n",
       "      <td>4</td>\n",
       "      <td>0.643066</td>\n",
       "      <td>Richard Nixon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>Richard</td>\n",
       "      <td>2</td>\n",
       "      <td>38684</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>0.633348</td>\n",
       "      <td>Richard Nixon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lastname firstname  Ccode  Word Count  year  quarter        cc  \\\n",
       "0    Nixon   Richard      2       44891  1969        1  0.655584   \n",
       "1    Nixon   Richard      2       38086  1969        2  0.638992   \n",
       "2    Nixon   Richard      2       30950  1969        3  0.594854   \n",
       "3    Nixon   Richard      2       27926  1969        4  0.643066   \n",
       "4    Nixon   Richard      2       38684  1970        1  0.633348   \n",
       "\n",
       "            name  \n",
       "0  Richard Nixon  \n",
       "1  Richard Nixon  \n",
       "2  Richard Nixon  \n",
       "3  Richard Nixon  \n",
       "4  Richard Nixon  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixon = pd.read_csv('nixon_quarter.csv')\n",
    "nixon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nixon['Quarter'] = nixon['year'].map(str) + \"-Q\" + nixon['quarter'].map(str)\n",
    "nixon['date'] = pd.to_datetime(nixon['Quarter'].str.replace(r'(Q\\d) (\\d+)', r'\\2-\\1'), errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(nixon['Quarter'], nixon['cc'])\n",
    "plt.xticks(rotation = 45, fontsize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US/UK Leader Differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ukleader = pd.read_csv('uk_leader_lta.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distrust    0.203785\n",
       "igb         0.144785\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usleader[['distrust', 'igb']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distrust    0.140793\n",
       "igb         0.105861\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ukleader[['distrust', 'igb']].mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
